DosePET v1.0 — initial public release

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README.md

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+---
+license: cc-by-nc-4.0
+pretty_name: DosePET - An Open-Source Dataset with 2100 Radiation Dose Maps for PET/CT
+tags:
+  - medical-imaging
+  - dosimetry
+  - pet-ct
+  - monte-carlo
+  - nuclear-medicine
+  - radiation-dose
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: organ_doses
+    data_files: metadata/dose_reports.parquet
+---
+
+# DosePET: An Open-Source Dataset with 2100 Radiation Dose Maps for PET/CT
+
+Voxel-level absorbed dose maps for **2100 whole-body and total-body PET/CT
+studies**, computed
+with GATE Monte Carlo simulation, together with **117 TotalSegmentator organ
+masks** and per-organ dose statistics for every study.
+
+| | |
+|---|---|
+| studies | 2100 (1503 [18F]FDG, 597 PSMA) |
+| isotopes | F-18 (1872), Ga-68 (228) |
+| organs per study | 117 |
+| organ dose rows | 245,700 |
+| unpacked size | 509 GB |
+| download size | 383 GB (all), **32 MB** for the organ dose tables alone |
+
+> The per-organ dose statistics
+> for all 2100 studies are a 32 MB download, and are also browsable in the
+> Dataset Viewer above. See [Quick start](#quick-start).
+
+---
+
+## Quick start
+
+```bash
+pip install huggingface_hub                       # recommended, not required
+wget https://huggingface.co/datasets/eltzanis/DosePET/resolve/main/download_dataset.py
+
+python3 download_dataset.py list                  # what's available
+
+python3 download_dataset.py get --content tables --out ./petct   # 32 MB
+python3 download_dataset.py get --sample 5        --out ./petct  # 5 full studies
+python3 download_dataset.py get --subset PSMA     --out ./petct  # one tracer
+python3 download_dataset.py get --all             --out ./petct  # everything
+
+python3 download_dataset.py get --all --dry-run   # size check, no download
+```
+
+Every selection extracts into the same layout, so you can start small and add
+more later without reorganising anything.
+
+---
+
+## Downloading exactly what you need
+
+
+### 1. Only the organ dose tables — 32 MB
+
+Every `dose_report.csv`, `metadata.json`, `dosimetry_config.json` and
+`segmentation/statistics.json`, for all 2100 studies. No dose images, no masks.
+
+```bash
+python3 download_dataset.py get --content tables --out ./petct
+```
+
+The same data is
+in `metadata/dose_reports.parquet` (24 MB, 245,700 rows) if you would rather
+have one table than 2100 files:
+
+```python
+import pandas as pd
+df = pd.read_parquet("hf://datasets/eltzanis/DosePET/metadata/dose_reports.parquet")
+df[df.organ == "liver"].groupby("isotope_key").mean_dose_rate_at_scan_mGy_per_h.describe()
+```
+
+### 2. Named studies — only their bytes are fetched
+
+```bash
+# find the ids
+python3 download_dataset.py list --cases
+
+# fetch three of them, from three different shards
+python3 download_dataset.py get \
+    --cases FDG/0001,FDG/0500,PSMA/PSMA_0179419e313f7d8c/05-03-2002-NA-PETCT_whole-body_PSMA-40379 \
+    --out ./petct
+```
+
+The dose shards are uncompressed tars and the manifest records each study's
+byte offset, so the script issues HTTP Range requests and pulls only what you
+asked for:
+
+```
+range-fetching 1 case(s), 144.04 MB of 9.91 GB
+range-fetching 1 case(s), 133.40 MB of 9.94 GB
+range-fetching 1 case(s), 105.14 MB of 9.94 GB
+done: 366 files
+```
+
+For a long list use a file:
+
+```bash
+printf 'FDG/0001\nFDG/0002\nFDG/0500\n' > my_cases.txt
+python3 download_dataset.py get --case-list my_cases.txt --out ./petct
+```
+
+Add `--no-range` to download whole shards instead (useful behind a proxy that
+strips Range headers; the script falls back to this automatically anyway).
+
+### 3. A representative sample
+
+```bash
+python3 download_dataset.py get --sample 10 --out ./petct
+```
+
+Ten studies spread evenly across the dataset rather than ten consecutive ids.
+Combine with `--subset` to sample one tracer:
+
+```bash
+python3 download_dataset.py get --sample 10 --subset PSMA --out ./petct
+```
+
+### 4. Masks and tables but no dose images
+
+```bash
+python3 download_dataset.py get --content tables,seg --out ./petct
+```
+
+All 245,700 AI generted organ masks for all 2100 studies: **5.3 GB**
+
+### 5. One tracer
+
+```bash
+python3 download_dataset.py get --subset FDG  --out ./petct    # 1503 studies
+python3 download_dataset.py get --subset PSMA --out ./petct    #  597 studies
+```
+
+### 6. By shard, for batching a big transfer
+
+```bash
+python3 download_dataset.py get --subset FDG --shards 0-3  --out ./petct
+python3 download_dataset.py get --subset FDG --shards 4-7  --out ./petct
+```
+
+`FDG_dose_007.tar` and `FDG_seg_007.tar.gz` hold exactly the same studies, so a
+shard index always gives you complete studies.
+
+### 7. Everything
+
+```bash
+python3 download_dataset.py get --all --out ./petct        
+```
+
+### Always check first
+
+```bash
+python3 download_dataset.py get --all --dry-run
+python3 download_dataset.py get --cases FDG/0001,FDG/0002 --dry-run
+```
+
+`--dry-run` prints the exact download size and the shards involved, and
+downloads nothing.
+
+### Measured download sizes
+
+| what you ask for | download |
+|---|---|
+| `--content tables` | 32 MB |
+| one named study | 333 MB |
+| `--sample 5` | 1.46 GB |
+| `--sample 10` | 2.80 GB |
+| `--sample 50` | 13.5 GB |
+| `--content tables,seg` (all masks, no dose) | 5.32 GB |
+| `--subset PSMA` | 68.2 GB |
+| `--subset FDG` | 314.7 GB |
+| `--all` | 382.8 GB |
+
+A named study costs more than its dose image alone (175 MB) because the masks
+for its shard arrive as one 150 MB gzipped archive and the 32 MB tables shard
+comes with it.
+
+---
+
+## Coming back for more later
+
+**Point `--out` at the same directory and the dataset grows in place.** Nothing
+is re-downloaded, nothing already on disk is touched.
+
+```bash
+python3 download_dataset.py get --content tables --out ./petct   # today:   32 MB
+python3 download_dataset.py get --cases FDG/0001 --out ./petct   # later:  333 MB
+python3 download_dataset.py get --sample 50      --out ./petct   # later: 13.5 GB
+python3 download_dataset.py get --all            --out ./petct   # finally: 383 GB
+```
+
+
+Two things worth knowing:
+
+- Each archive is **deleted after it is extracted**, to save disk. If a
+  download is interrupted mid-shard it restarts that shard; pass
+  `--keep-archives` to keep them and make interruptions cheaper.
+- The tables shard (32 MB) covers all 2100 studies, so it comes down again
+  with each new batch. Harmless, but it is why a "three more studies" batch
+  reports ~867 MB rather than ~836 MB.
+
+---
+
+## What you end up with
+
+The extracted tree is **identical to the source dataset** — same directory
+structure, same filenames, same bytes. Verified by extracting a complete shard
+pair and diffing it against the original:
+
+```
+directory tree : IDENTICAL - 2976 paths, same names, same nesting
+file contents  : ALL 2928 FILES BYTE-IDENTICAL
+```
+
+Partial downloads give you a correct subtree: take only the
+tables and you get `FDG/0001/dose_report.csv` in its proper place with
+`dose_image.nii.gz` simply absent. Add masks or dose images later and they land
+in the same tree.
+
+The one thing not preserved is **file modification times** — they are set to
+when you extracted, not when the data was produced. File contents are bit-for-bit
+identical.
+
+### Integrity
+
+Every archive's SHA-256 is checked against `shards.json` before extraction, and
+the download aborts on a mismatch rather than extracting corrupt data. To
+re-check archives you kept with `--keep-archives`:
+
+```bash
+python3 download_dataset.py verify --cache ./petct_cache
+```
+
+---
+
+## What each study contains
+
+```
+FDG/0001/
+    dose_image.nii.gz            total absorbed dose, Gy, uint16 + scl_slope
+    dose_report.csv              117 rows, per-organ dose statistics
+    metadata.json                isotope, half-life, injected activity, timings
+    dosimetry_config.json        cumulated activity, primaries, integration mode
+    segmentation/
+        liver.nii.gz  ...        117 binary organ masks (TotalSegmentator)
+        statistics.json          mask volumes
+
+PSMA/<patient>/<scan>/           same files, one extra directory level
+```
+
+### `dose_image.nii.gz`
+
+Each voxel is the **total absorbed dose in Gy**, integrated from the start of
+the scan to infinity assuming **physical decay only** — the activity
+distribution is frozen in space, with no biological clearance and no
+redistribution.
+
+Stored as `uint16` with a `scl_slope` in the NIfTI header (quantisation step =
+`max_dose / 65535`, about 1.5e-5 relative). **You must apply `scl_slope`.**
+`nibabel` and `SimpleITK` do this automatically:
+
+```python
+import nibabel as nib, numpy as np
+dose_Gy = np.asanyarray(nib.load("FDG/0001/dose_image.nii.gz").dataobj)  # Gy
+```
+
+### `dose_report.csv`
+
+One row per organ, alphabetical:
+
+| column | meaning |
+|---|---|
+| `organ` | TotalSegmentator label |
+| `mean_dose_Gy`, `max_dose_Gy`, `min_dose_Gy`, `dose_std_Gy` | dose in the mask |
+| `mean_dose_injection_Gy`, `max_dose_injection_Gy` | back-extrapolated to injection time |
+| `volume_cm3`, `mass_g`, `num_voxels` | mask geometry |
+| `dcf_Gy_per_Bqs` | dose conversion factor, Gy per decay in the FOV |
+| `mean_dose_rate_at_scan_mGy_per_s`, `mean_dose_rate_at_scan_mGy_per_h` | dose rate at scan start |
+
+All 245,700 rows are also published as `metadata/dose_reports.parquet` for the
+Dataset Viewer, joined with each study's isotope, half-life, injected activity
+and cumulated activity.
+
+In the Parquet table the dose conversion factor is given as
+**`dcf_fGy_per_Bqs`** — the same quantity as the CSVs' `dcf_Gy_per_Bqs`, in
+femtogray (multiplied by 10^15^). Doses back-extrapolated to injection time are provided in
+each study's `dose_report.csv`, and simulation parameters in its
+`dosimetry_config.json`.
+
+A zero in `dcf_fGy_per_Bqs` (8857 rows) means the organ mask is empty because
+the structure lies outside the acquired field of view, not that the value is
+missing.
+
+---
+
+## Deriving dose rate and Gy-per-decay
+
+Because the only time dependence is a single exponential shared by every voxel,
+both quantities are the dose image times **one scalar per study**:
+
+```
+lambda   = ln2 / T_half                                  T_half from metadata.json
+rate     = D * 3.6e6 * lambda      [mGy/h]  at scan start
+DCF      = D / A_tilde             [Gy per decay]        A_tilde = cumulated_from_scan
+```
+
+```python
+import json, math, numpy as np, nibabel as nib
+case = "FDG/0001"
+cfg  = json.load(open(f"{case}/dosimetry_config.json"))
+meta = json.load(open(f"{case}/metadata.json"))
+T_half  = cfg.get("half_life_seconds") or meta["isotope"]["half_life_seconds"]
+A_tilde = cfg["cumulated_from_scan"]
+
+D = np.asanyarray(nib.load(f"{case}/dose_image.nii.gz").dataobj)   # Gy
+rate_mGy_per_h = D * 3.6e6 * math.log(2) / T_half
+dcf_Gy_per_dec = D / A_tilde
+```
+
+For organ-level numbers you need none of this — the columns are already in
+`dose_report.csv`.
+
+### `tools/convert_dose_images.py`
+
+It does the conversions above for you, and can
+independently re-verify the published tables.
+
+```bash
+DATA=./petct            # where you extracted the dataset
+
+# voxel-wise dose-rate maps, mGy/h at scan start
+python3 tools/convert_dose_images.py rate --root $DATA/FDG --out ./rate_maps
+
+# voxel-wise Gy-per-decay maps
+python3 tools/convert_dose_images.py dcf  --root $DATA/PSMA --out ./dcf_maps
+
+# per-organ table straight from the image + masks, in the same column
+# order as dose_report.csv so the two can be diffed
+python3 tools/convert_dose_images.py organs --case $DATA/FDG/0001 --out table.csv
+
+# recompute every reproducible column and compare against the published CSV
+python3 tools/convert_dose_images.py check --root $DATA/FDG
+```
+
+Other time points are opt-in: `--at injection` (uses the stored injection
+correction factor) or `--at +3600` for one hour after the scan. Units via
+`--unit mGy_per_h | mGy_per_s | Gy_per_s`.
+
+**The conversion is lossless.** By default the tool copies the `uint16` payload
+byte for byte and multiplies only the NIfTI `scl_slope`, so the output carries
+no extra quantisation and is the same size as the input. Use `--mode float32`
+if your software ignores `scl_slope`.
+
+`check` is the reproducibility anchor: it recomputes organ means from the dose
+image and the masks, applies the scale factor, and diffs against the shipped
+`dose_report.csv`, printing PASS/FAIL per study:
+
+```
+[1/2] PASS  0001  117 organs, 1283 values, worst 0.499 x tolerance
+```
+
+Full usage in `tools/README.md`. Requires `numpy`, `nibabel`, and `SimpleITK`
+for the mask-based commands.
+
+> Converting a whole subset duplicates it on disk (hundreds of GB). Unless you
+> need the files on disk, apply the scalar on the fly with the six lines above.
+
+---
+
+## How the release is packaged
+
+Three kinds of shard, all tar archives whose members keep their original
+relative paths:
+
+| shard | contents | compression | why |
+|---|---|---|---|
+| `data/tables.tar.gz` | every `.csv` / `.json`, all 2100 studies | gzip | 32 MB, so table-only users download almost nothing |
+| `data/<subset>/<subset>_dose_NNN.tar` | `dose_image.nii.gz` | **none** | already gzipped NIfTI |
+| `data/<subset>/<subset>_seg_NNN.tar.gz` | organ masks | gzip | masks compress about **23:1** (115 GB → 5 GB) |
+
+`*_dose_NNN` and `*_seg_NNN` with the same `NNN` hold **exactly the same
+studies**, so a complete slice of the dataset is two downloads.
+
+Alongside the data:
+
+| path | what |
+|---|---|
+| `download_dataset.py` | the download script |
+| `shards.json` | manifest: sizes, SHA-256, case lists, per-case byte offsets |
+| `metadata/dose_reports.parquet` | all 245,700 organ rows, for the Dataset Viewer |
+| `tools/convert_dose_images.py` | dose-rate / Gy-per-decay conversion + verification |
+| `tools/README.md` | usage guide for the conversion tool |
+| `skills/dosepet-research/` | agent skill: instructions for AI agents working with the dataset |
+
+`shards.json` lists every shard with its size, SHA-256 and case list, and maps
+each case id to its shards. The download script verifies every archive against
+that checksum before extracting.
+
+---
+
+## Using DosePET with AI agents
+
+`skills/dosepet-research/` is an agent skill — a set of instructions that an
+agentic AI system (for example Claude Code or OpenAI Codex) can load to work
+with this dataset correctly. It covers selective downloading, retrieving and
+matching the source PET/CT examinations from ENHANCE.PET and
+PSMA-PET-CT-Lesions, verifying spatial correspondence before use, and the
+physical meaning and units of the dose quantities.
+
+```bash
+hf download eltzanis/DosePET --repo-type dataset --include "skills/*" --local-dir .
+```
+
+Then make the `skills/dosepet-research` folder available to your agent (for
+Claude Code, copy it into `~/.claude/skills/` or the project's
+`.claude/skills/`).
+
+---
+
+## Assumptions and caveats
+
+1. **Physical decay only.** No biological clearance or redistribution.  
+2. **Time zero is scan start.** 
+3. **`dcf_Gy_per_Bqs` is normalised to decays in the imaged FOV**, with that
+   patient's own tracer distribution as the source. It is a FOV-normalised
+   S-value, *not* dose per decay in a specific source organ, and should not be
+   compared directly with MIRD/OLINDA organ-to-organ S-values.
+4. **Apply `scl_slope`** (if it is not applied automatically) when reading the dose images.
+5. **Four FDG studies** (`0718`, `1022`, `1195`, `1289`) have no injection
+   correction factor, because the acquisition time is missing upstream. Their
+   `dose_report.csv` therefore omits `mean_dose_injection_Gy` and
+   `max_dose_injection_Gy`. Scan-time dose, dose rate and DCF are unaffected.
+
+---
+
+
+
+## Citation
+
+If you use DosePET in your research, please cite:
+
+```bibtex
+@article{Tzanis2026DosePET,
+  title  = {DosePET: An Open-Source Dataset with 2100 Radiation Dose Maps for PET/CT},
+  author = {Eleftherios Tzanis and Antonios Tzortzakakis and Michail E. Klontzas},
+  year   = {2026},
+  note   = {Manuscript under review}
+}
+```
+
+
+## License
+
+**CC BY-NC 4.0** — Creative Commons Attribution-NonCommercial 4.0
+International. You may share and adapt this dataset for **non-commercial**
+purposes with attribution. See https://creativecommons.org/licenses/by-nc/4.0/
+
+The underlying PET/CT images come from their respective source collections;
+please also honour their terms and cite them.
+
+- **FDG subset** — ENHANCE.PET whole-/total-body [18F]FDG-PET/CT dataset:
+
+  Ferrara D, Pires M, Gutschmayer S, et al. Sharing a whole-/total-body
+  [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET
+  initiative. *Sci Data* 13, 869 (2026).
+  https://doi.org/10.1038/s41597-026-07218-y
+
+- **PSMA subset** — LMU Munich whole-body PSMA-PET/CT dataset:
+
+  Jeblick K, Schachtner B, Mittermeier A, et al. A Whole-Body PSMA-PET/CT
+  dataset with manually annotated tumor lesions. *Sci Data* 13, 1023 (2026).
+  https://doi.org/10.1038/s41597-026-07821-z
+
+

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